Summary

When ApplicativeDo is turned on, GHC will use a different method for desugaring do-notation, which attempts to use the Applicative operator <*> as far as possible, along with fmap and join.

ApplicativeDo makes it possible to use do-notation for types that are Applicative but not Monad. (See examples below).

For a type that is a Monad, ApplicativeDo implements the same semantics as the standard do-notation desugaring, provided <*> = ap for this type.

ApplicativeDo respects RebindableSyntax: it will pick up whatever <*>, fmap, and join are in scope when RebindableSyntax is on.

Motivation

Some Monads have the property that Applicative bind is more
efficient than Monad bind. Sometimes this is really
important, such as when the Applicative bind is
concurrent whereas the Monad bind is sequential (c.f. ​Haxl). For
these monads we would like the do-notation to desugar to
Applicative bind where possible, to take advantage of the improved
behaviour but without forcing the user to explicitly choose.

Applicative syntax can be a bit obscure and hard to write.
Do-notation is more natural, so we would like to be able to write
Applicative composition in do-notation where possible. For example:

(\x y z -> x*y + y*z + z*x) <$> expr1 <*> expr2 <*> expr3

vs.

do x <- expr1; y <- expr2; z <- expr3; return (x*y + y*z + z*x)

Example 1

do
x <- a
y <- b
return (f x y)

This translates to

(\x y -> f x y) <$> a <*> b

Here we noticed that the statements x <- a and y <- b are independent, so we can make an Applicative expression. Note that the desugared version uses the operators <$> and <*>, so its inferred type will mention Applicative only rather than Monad. Therefore this do block will work for a type that is Applicative but not Monad.

Example 2

If the final statement does not have a return, then we need to use join:

do
x <- a
y <- b
f x y

Translates to

join ((\x y -> f x y) <$> a <*> b)

Since join is a Monad operation, this expression requires Monad.

Example 3

do
x1 <- A
x2 <- B
x3 <- C x1
x4 <- D x2
return (x1,x2,x3,x4)

Here we can do A and B together, and C and D together. We could do it like this:

Example 4

Now we have a dependency: y depends on x, but there is still an opportunity to use Applicative since z does not depend on x or y. In this case we end up with:

(\(x,y) z -> f x y z) <$> (do x <- A; y <- B x; return (x,y)) <*> C

Note that we had to introduce a tuple to return both the values of x and y from the inner do expression

It's important that we keep the original ordering. For example, we don't want this:

do
(x,z) <- (,) <$> A <*> C
y <- B x
return (f x y z)

because this has a different semantics from the standard 'do' desugaring; a Monad that cares about ordering will expose the difference.

Another wrong result would be:

do
x <- A
(\y z -> f x y z) <$> B x <*> C

Because this version has less parallelism than the first result, in which A and B could be performed at the same time as C.

Example 5

In general, ApplicativeDo might have to build a complicated nested Applicative expression.

do
x1 <- a
x2 <- b
x3 <- c x1
x4 <- d
return (x2,x3,x4)

Here we can do a/b/d in parallel, but c depends on x1, which makes things a bit tricky: remember that we have to retain the semantics of standard do desugaring, so we can't move the call to c after the call to d.

We can write this expression in a simpler way using | for applicative composition (like parallel composition) and ; for monadic composition (like sequential composition): ((a | b) ; c) | d.

Note that this isn't the only good way to translate this expression, this is also possible: (a ; (b | c)) | d. It's not possible to know which is better. ApplicativeDo makes a best-effort attempt to use parallel composition where possible while retaining the semantics of the standard 'do' desugaring.

Related proposals

Implementation

The implementation is tricky, because we want to do a transformation that affects type checking (and renaming, because we might be using RebindableSyntax), but we still want type errors in terms of the original source code. Therefore we calculate everything necessary to do the transformation during renaming, but leave enough information behind to reconstruct the original source code for the purposes of error messages.